Objective Apnea is common among infants in the neonatal intensive care unit (NICU). Our group previously developed an automated algorithm to quantitate central apneas with associated bradycardia and desaturation (ABDs). Sex differences in lung disease are well described in preterm infants, but the influence of sex on apnea has not been established. Study Design This study includes infants < 34 weeks' gestation admitted to the University of Virginia NICU from 2009 to 2014 with at least 1 day of bedside monitor data available when not on mechanical ventilation. Waveform and vital sign data were analyzed using a validated algorithm to detect ABD events of low variance in chest impedance signal lasting at least 10 seconds with associated drop in heart rate to < 100 beats/minute and drop in oxygen saturation to < 80%. Male and female infants were compared for prevalence of at least one ABD event during the NICU stay, treatment with caffeine, occurrence of ABDs at each week of postmenstrual age, and number of events per day. Results Of 926 infants studied (median gestational age 30 weeks, 53% male), median days of data analyzed were 19 and 22 for males and females, respectively. There was no sex difference in prevalence of at least one ABD event during the NICU stay (males 62%, females 64%, p = 0.47) or in the percentage of infants treated with caffeine (males 64%, females 67%, p = 0.40). Cumulative prevalence of ABDs from postmenstrual ages 24 to 36 weeks was comparable between sexes. Males had 18% more ABDs per day of data, but this difference was not statistically significant (p = 0.16). Conclusion In this large cohort of infants < 34 weeks' gestation, we did not detect a sex difference in prevalence of central ABD events. There was a nonsignificant trend toward a greater number of ABDs per day in male infants. Key Points

Objectives: Early detection of subacute potentially catastrophic illnesses using available data is a clinical imperative, and scores that report risk of imminent events in real time abound. Patients deteriorate for a variety of reasons, and it is unlikely that a single predictor such as an abnormal National Early Warning Score (NEWS) will detect all of them equally well. The objective of this study was to test the idea that the diversity of reasons for clinical deterioration leading to ICU transfer mandates multiple targeted predictive models. Design: Individual chart review to determine the clinical reason for ICU transfer; determination of relative risks of individual vital signs, lab tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer; logistic regression modeling for the outcome of ICU transfer for a specific clinical reason. Setting: Cardiac medical-surgical ward; tertiary care academic hospital. Patients: 8111 adult patients, 457 of whom were transferred to an ICU for clinical deterioration. Interventions: None. Measurements and main results: We calculated the contributing relative risks of individual vital signs, lab tests and cardiorespiratory monitoring measures for prediction of each clinical reason for ICU transfer, and used logistic regression modeling to calculate ROC areas and relative risks for the outcome of ICU transfer for a specific clinical reason. The reasons for clinical deterioration leading to ICU transfer were varied, as were their predictors. For example, the three most common reasons - respiratory instability, infection and suspected sepsis, and heart failure requiring escalated therapy - had distinct signatures of illness. Statistical models trained to target specific reasons for ICU transfer performed better than one model targeting combined events, and both performed better than the untrained NEWS score. Conclusions and relevance: A single predictive model for clinical deterioration does not perform as well as having multiple models trained for the individual specific clinical events leading to ICU transfer.

Objective: Signatures of illness in vital signs of Neonatal Intensive Care Unit (NICU) patients can inform on future adverse events and outcomes. We implemented highly comparative time-series analysis to discover features and predictive analytics tools for all-cause mortality in the next 7 days, using the ubiquitous HR and SpO2 vital sign data from bedside monitors. Design: We populated a Time Series Commons with the complete HR and SpO2 data from all infants in the University of Virginia NICU from 2009 to 2019. We calculated the results of applying over 80 members of 11 mathematical families to random ten-minute segments of 0.5Hz data each day for each infant, with varying parameter sets, resulting in 4998 algorithmic operations on each infant. We used an unsupervised mutual information-based method to cluster the results, and we selected a single representative operation from each cluster. We used our FAIRSCAPE framework to compute a detailed provenance of all computations, and we constructed a complete software library with links to the analyzed data for reproducibility and reuse. We made multivariable logistic regression models using the lasso to assay the usefulness of the algorithms. Setting: Neonatal ICU. Patients: 5957 NICU infants, of whom 206 died. Measurements and main results: 3555 algorithmic operations returned usable results. Twenty representative operations, selected from each of 20 unsupervised clusters, held more than 81% of the information predicting death. A multivariable model had an AUC of 0.81 for predicting death in the next 7 days. In addition, five algorithms outperformed others: moving threshold, successive increases, surprise, and a random walk model. Conclusions: Highly comparative time-series analysis revealed new vital sign metrics to identify NICU patients at the highest risk of death in the next week. This approach can facilitate the discovery of signatures of impending, potentially actionable, clinical decompensation in monitored patients.

Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.